Understanding the Role of Description, Prediction, and (Causal) Explanation
A Fancy Name for an Old Way of Thinking
\[E[Y|T=1] - E[Y|T=0] = \underbrace{E[Y_1 - Y_0|T=1]}_{ATT} + \underbrace{\{ E[Y_0|T=1] - E[Y_0|T=0] \}}_{BIAS}\]
— Facure Alves (2022)
Our results suggest that “Schrödinger’s causal inference,” — where studies avoid stating (or even explicitly deny) an interest in estimating causal effects yet are otherwise embedded with causal intent, inference, implications, and recommendations — is common.
— Haber et al. (2022)
Understanding the Type of Questions We’re Asking & the Different Methods for Answering Them
Contact:
Code & Slides:
Paul Johnson // Introduction to Causal Inference // Oct 3, 2024